I had a few people ask for suggestions on AI/ML learning resources after my post last week. I thought I would share some of my favorites. I have broken the resources up into three areas; for everyone, business power users, and technical.  All of these resources have free content or are entirely free.

For Everyone:

• While the topic of AI/ML can be deeply technical, there is room for every professional to keep pace. The biggest challenge seems to be the speed of change within the industry. I have found unbiased and curated content essential in my efforts to stay up to speed. Dr. Alan Thompson @alandthompson produces a lot of AI content, however I review his monthly memo religiously. “The Memo” is sent to subscribers on a monthly basis and strives to document the evolution of AI over that month. He does this by focusing on the “big” and the “interesting” things that occurred in that month. Even as someone that is employed to stay on top of AI, there hasn’t been a month that has gone by where I haven’t learned something new from Dr. Thompson. https://lifearchitect.ai/memo/
• DeepLearning.AI is a technical education company that is focused on AI. While there are others in this space, DeepLearning is setting the pace. They have been around for a handful of years and built a deep set of courses to pick from. There are classes ranging from “AI for Everyone” to “Mathematics for Machine Learning and Data Science Specialization” to “TensorFlow Advanced” and “Generative Adversarial Networks (GANs) Specialization”. @andrewyng is well known within the industry and provides instruction on many of their courses. The courses are all hosted on Coursera so you’re likely familiar with the platform. Many of the introductory courses are a handful of hours, which is the sweet spot in my experience (60 mins of YouTube videos are rarely enough and sitting a 16-week course as a professional can be unrealistic: https://www.deeplearning.ai/short-courses/
• Whether you’re looking to expand your conversational knowledge OR looking for information on how you might apply LLM’s professionally, I’d highly recommend investing a handful of hours on DeepLearning’s “Generative AI for Everyone”. You will leave the course knowing a lot more about LLM’s, what it looks like to customize an LLM to use professionally, and how businesses are adapting to generative AI with success. https://www.coursera.org/learn/generative-ai-for-everyone

For Business Power User:

In my opinion, there are two reasons to get business power users engaged with AI. 1) 3rd party generative AI tools are gaining notoriety and have significant positive impacts on white collar professionals (assuming they follow their employers AI use policies). 2) Beyond generative AI, businesses will see the next major AI success from Intelligent Automation. This automation requires a deep understanding of the businesses processes, workflows, and informal practices, which IT can not champion. The business professionals that take the time now to learn about AI will be the ones helping sculpt this intelligent automation within their workplace. Beyond what was found above in the “For Everyone” section, I recommend the following to customers looking to up-skill business users:

• University of Pennsylvania’s Wharton school of business has put their AI business classes on Coursera for free. You will spend four weeks with up to 10 hours a week working through the course. Initially they’ll introduce you to the fundamental concepts of AI and ML. Then they transition into actual use cases and examples. With how the course is structured, after the first week, you could skip around to the topics of interest, although I suggest you do not miss out on the Data Analysis for Non-data scientists in the third week: https://www.coursera.org/specializations/ai-for-business-wharton
• When friends and family tell me they’re starting to use one of the LLM products, I push them towards learning how to properly prompt an LLM. Prompting is vitally important and has little to no learning curve. I like to compare this to using excel. Informal prompting is like using excel as a simple table whereas building a proper prompt is like building complex logic across tabs with functions – it’s the same software but two totally different outputs. https://learnprompting.org/docs/intro
• As I stated above, it is my opinion that business professionals should be preparing for the next enterprise application of AI, intelligent automation. While not strictly AI, learning the basics of business process management, the tools used by business analysts, and otherwise preparing yourself to be process minded, will provide great career value in the near future. While there are many automation platforms out there, UiPath is a leader and is all-in with AI and intelligent automation as a future. While the following link does have some UiPath specific content, much of the course is just based on learning the craft of process analysis/mapping. https://academy.uipath.com/learning-plans/automation-business-analyst-foundation

For Technical:

If you've made it this far into the post, and you're technical, you don't need me to preface this section. You just want the links ;)

• Data Talks Club is a community built by Alexey Grigorev for data professionals. It has been my go to resource lately. They publish articles, host events, produce a podcast, coordinate a weekly book author AMA, and create training course. Their training courses are offered in a self paced and a Cohort lead style. They have a MLOps cohort class starting in May that I’m really looking forward to: https://github.com/DataTalksClub/mlops-zoomcamp
• If you don’t have access to the Microsoft Azure AI tools, there are other hands-on training that is worth taking. Full Stack has built a training that has 11 lectures. It is posted on YouTube and focuses on how to build an application powered by large language models: https://www.youtube.com/playlist?list=PL1T8fO7ArWleyIqOy37OVXsP4hFXymdOZ
• Realizing that many technical team members are new to the data scientist stuff, it’s worth a good overview of using things like PyTorch and TensorFlow. Microsoft’s Learn platform has a lot of great information, including this: https://learn.microsoft.com/en-us/training/modules/train-evaluate-deep-learn-models/
• If you’re more like me and like to learn hands on, Microsoft has also built some training classes around building some specific product within their Azure AI products: https://learn.microsoft.com/en-us/training/paths/extract-data-from-forms-document-intelligence/ https://learn.microsoft.com/en-us/training/paths/develop-language-solutions-azure-ai/ https://learn.microsoft.com/en-us/training/modules/python-flask-build-ai-web-app/

I can’t help but reflect on my own journey in learning AI and ML. It started in graduate school many years ago, implementing ML and NLP in my thesis project. As an industry, we’ve come a long way since then. Even the way we talk about AI and ML has changed a lot. However, it has been a rewarding process. I hope these resources can help others on their journey. I had a lot of help when I first started. If anyone has questions, let me know and I’ll be happy to answer. Alternatively, if I’ve missed a valuable resource, please leave a comment.

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